In a material (or chemical) screen, the aim is to select from a large number of candidate materials the material or set of materials that perform best in some particular test. This test could be a laboratory experiment or as in our case a computer simulation. Bayesian optimization has previously been suggested as a way to improve the efficiency of such screens, dramatically reducing the number of tests necessary to find the best performing materials. See for example [1]}, [2]}, [3]}. Bayesian optimization is a family of methods for optimizing the output of a black-box function, which work by modeling the input-output relationship of the function as a random variable. Applied to a materials screen, Bayesian optimization works by iteratively selecting new materials for testing whilst simultaneously using the data generated by the previous tests to model the relationship between known properties of the candidate materials and their performance in the test. The model's predictions are then used to guide further experimentation.
